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Compared with the traditional light field rendering, the neural radiance field can use the neural network to fit the ray samples of the scene, and implicitly encode the light field of the input image to synthesize a new view. An improved neural radiance field view synthesis method has been proposed to reduce the training time and accelerate the rendering speed based on pixel adaptive selection and trapezoidal numerical integration. In the pixel selection stage, the pixel adaptive selection method guided by the rendering loss can reasonably allocate the positions of pixel samples, so that the regions with unclear rendering or higher details can have more iterations. In the ray sampling stage, a volume rendering integration formula using the trapezoidal numerical integration is proposed. Combined with the network structure of the neural radiance field, the trapezoidal numerical integration is used in the coarse network stage, and the rectangular numerical integration is used in the fine network stage. It ensures the quality of view synthesis while reducing the number of uniform ray samples. Training and testing experiments on Realistic Synthetic 360 °dataset and Real Forward-Facing dataset. Experimental results show that compared with the neural radiance field method, this method reduces training time by 23% and improves view synthesis efficiency by 25% while achieving similar view synthesis quality
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